To evaluate the performance of TRUST-LAPSE on drift detection, we simulate 1000 data streams of 10,000 data points each for each domain, with injected change points as explained in Section REF .
We then calculate the sequential mistrust scores using the two-sided Mann-Whitney test [1]} at a significance level of \(0.05\)  for each sample's latent-space mistrust score in the stream and run it through our sequential detector with window sizes \(w_A = w_B = 25\)  for vision experiments and \(w_A = w_B = 50\)  for audio and EEG experiments (Remark REF ).
We perform a 2-cluster KMeans [2]} on the generated Mann-Whitney scores to assign trust and flag (mistrust) actions.
We define the detector to have erred if it wrongly concludes that an incoming test sample's prediction is to be flagged (assigned high mistrust) when it was actually supposed to be trusted
or vice-versa.
If the framework detects the change point within its fixed window size, we do not consider it as an error.
For each data stream, we then add the errors of all samples to calculate detection error. We repeat this for all \(N = 1000\)  streams to estimate the detector's error distribution across streams. Fig. REF  shows the error distribution over EEG data streams.
Over \(73\%\)  of the streams have less than \(10\%\)  error and over \(93\%\)  have less than \(20\%\)  error.
For our audio domain, over \(85\%\)  of the streams have less than \(10\%\)  error and over \(97\%\)  have less than \(20\%\)  error.
For our vision tasks, the error distribution is much tighter and we are able to get near \(99\%\)  detection accuracy for over \(95\%\)  of the streams.
We show plots of generated trust scores from TRUST-LAPSE and other baselines for parts of audio data streams as examples in Figs. REF , REF  (more plots in Appendix, Figs. REF -REF ).
<FIGURE><FIGURE><FIGURE>